1. Field of the Invention
The present invention relates to an entirely new intelligence information processing system in which the operation of a neural network corresponding to human intuitive thinking and the operation of a serial processing-type computer corresponding to human logical thinking are combined together so as to permit the system to process information in a manner as close as possible to the manner in which the human brain functions.
2. Description of the Related Art
It is assumed that, instead of each neuron in the brain of a living being memorizing a specific piece of information, a vast number of neurons in a group cooperate with each other to simultaneously memorize a plurality of pieces of information in order to build a neural network model. It is also assumed that, in the course of an information process performed in the brain of a living being, an initial state input to each neuron is affected by combination patterns which are formed by stored information to voluntarily converge on a stable state (low state of the energy of a system), while a total computation of the input, a threshold process, and feedback are repeated.
It is therefore understood that when stored information is regarded as complete information, and when a given piece of incomplete information is input to each neuron, as shown in FIG. 1, a neuron voluntarily converges on stored information that is most similar to that input. After the neuron eventually reaches a stable state, the state of the neuron is output in the form of complete information. This is the principle of an associative memory based on a neural network model.
The following describes an example of an associative memory unit (associative memory) realized by a Hopfield model, one of the neural network models.
FIG. 2 is a view showing the structure of a conventional optical associative memory to which optical technology is applied. It is disclosed in a publication (material for a meeting for the study of optical/quantum electronics: OQE87-174, published by The Institute of Electronics, Information and Communication Engineers, 1988).
In FIG. 2, numerals 11a and 11b designate light emitting element arrays; 12a and 12b photo-masks (space optical modulating elements); 13a and 13b light receiving element arrays; 14 a differential amplifier; 15 a comparator; 16 an input unit; and 17 an output unit.
The operation of the associative memory will now be explained. Light beams having fan-like shapes are irradiated by the light emitting element arrays 11a and 11b toward the photo-masks 12a and 12b, respectively. If the state of each light emitting element is expressed as EQU X.sub.K (K=1, 2 . . . j, . . . i, . . . n).
and if X.sub.K is expressed as either a "1" or "0", depending on whether or not the respective light emitting element is lit, then the states inside the light emitting element arrays 11a and 11b can be given as follows: EQU X=(X.sub.1, X.sub.2, . . . X.sub.i . . . X.sub.j . . . X.sub.n).
where X is given by a vector, and n is the number of light emitting elements in the light emitting and receiving element arrays, corresponding to the number of neurons in this neural network.
Each of the photo-masks 12a and 12b is divided into n.times.n elements, and is constructed so that a light transmittance in each element can be separately altered. The light transmittance in each element is expressed by a matrix EQU T=[T.sub.ij ].
The states inside the light receiving element arrays 13a and 13b can be given in the same manner as in the vector X as follows: EQU U=(U.sub.1, U.sub.2, . . . U.sub.i, . . . U.sub.j, . . . U.sub.n).
If the jth light emitting element irradiates light toward the jth row of the photo-mask, and the light transmitted through the ith column of the photo-mask is received by the ith light receiving element, the ith light receiving element operates in correspondence to carrying out a vector/matrix multiplication which is expressed by the following equation: ##EQU1##
It is assumed in the neural network that the strength that combines the respective neurons serves to store information. In the above structure, the combining strength may be realized by the transmittance T in each of the n.times.n elements, into which the photo-masks 12a and 12b are divided. In other words, the transmittance T in the photo-masks 12a and 12b stores information. In the Hopfield model described hereinafter, an information storage law is given by the following equation: ##EQU2##
where
N is the quantity of stored information;
T.sub.ij =T.sub.ji ; and
T.sub.ii =0
Although T.sub.ij may assume both positive and negative values, it is difficult for T.sub.ij to optically assume negative values. In this embodiment, therefore, as shown in FIG. 2, two optical systems are manufactured, one corresponding to elements assuming positive values T.sub.ij and the other to those assuming negative values T.sub.ij. The differential amplifier 14 operates to generate a difference between the outputs of the light receiving element arrays 13a and 13b. This difference is given as follows: EQU U.sub.i =U.sub.i.sup.(+) -U.sub.i.sup.(-)
The signal output from the differential amplifier 14 is processed by the comparator 15 to perform the following threshold operation: EQU X.sub.i =.theta.(y)
where .theta.(y)=1(y&gt;0), 0(y.ltoreq.0) The output signal is then fed back to the light emitting element arrays 11a and 11.
With this construction, for example, when three different characters of information, which respectively correspond to "A", "J" and "E", are stored in the photo-masks 12a and 12b, even if incomplete information, for instance, "A'", is input from the input unit 6 to the light emitting element arrays 11a and 11b, the output, while repeating the feedback operation, converges on a stored character of information "A" that is closest to the input character of information "A'". Eventually, the output is sent by the output unit 17 in the form of a complete output "A".
The above process can be described by using the terms previously mentioned. The energy of the system assumes the minimum values of stored characters of information "A", "J". and "E". When incomplete information is fed, the on/off condition of the light emitting element arrays 11a and 11b is changed so as to assume the minimum value of the energy near the light emitting element arrays. This change causes the entire system to voluntarily change, similarly to a human associative function.
According to the conventional associative memory thus constructed, even when inappropriate results are associated, it does not correct such results. In other words, the conventional associative memory is associated merely with patterns of stored data that are correlated most closely to the input, and therefore does not perform an operation corresponding to the flexible thinking of the human brain, thus limiting the versatility of the memory.